Cigarette Smoking and Nicotine Dependence Trajectories Among Incident Adolescent Smokers
Bibliographic record
Abstract
INTRODUCTION: Few interventions target adolescent cigarette smokers to prevent escalation in cigarette use or promote cessation, in part because little is known about co-developing smoking and nicotine dependence (ND). Our objectives were to: (1) estimate developmental trajectories of ND/cravings, withdrawal symptoms, the modified Fagerström Tolerance Questionnaire (mFTQ) and ICD-10 tobacco dependence in incident adolescent smokers; (2) describe concordance in number and shapes of trajectories across the four ND indicators; and (3) classify participants in each ND trajectory according to cigarette smoking trajectories. METHODS: Data were drawn from an ongoing longitudinal investigation of 1294 grade 7 students recruited in 1999-2000 in 10 Montreal-area high schools. Group-based joint trajectory models were used to identify distinct subgroups defined by the four ND indicators, in 307 incident smokers. RESULTS: The optimal trajectory model included five groups for ND/craving and four groups for each of withdrawal symptoms, the mFTQ and ICD-10 tobacco dependence. The four ND indicators showed similar developmental patterns and classification into smoking trajectory groups, although some discordance was observed. Smokers in the low-level decreaser group and stable low consumers who exhibited high ND were younger than those in the cigarette-low ND trajectory groups. Moderate or rapid escalators who exhibited no/low ND were less likely to have university-educated mothers and more likely to have parents who smoke. CONCLUSIONS: Trajectories were similar across ND indicators, and generally reflected cigarette smoking trajectory shapes. Novice smokers may need education to become self-aware of developing ND symptoms, as well as to learn about alternative courses of action once ND symptoms manifest. IMPLICATIONS: Trajectories of cigarette smoking and ND symptoms have rarely been investigated concurrently. This study provides evidence of high concordance across four distinct ND indicators in the proportion of participants with no/low-level dependence, and with high or increasing ND. Moreover, the development of cigarette smoking is concordant with ND symptom development. Interventions to prevent escalation and promote cessation should target adolescents before first puff to increase self-awareness of developing ND symptoms, as well as to learn about alternative courses of action once ND symptoms are experienced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".